Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | AAMAS '24 |
| Untertitel | Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems |
| Herausgeber/-innen | Mehdi Dastani, Jaime Simao Sichman |
| Seiten | 1928-1937 |
| Seitenumfang | 10 |
| Publikationsstatus | Veröffentlicht - 6 Mai 2024 |
| Veranstaltung | 23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024 - Auckland, Neuseeland Dauer: 6 Mai 2024 → 10 Mai 2024 |
Publikationsreihe
| Name | Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS |
|---|---|
| ISSN (Print) | 1548-8403 |
Abstract
Using synthesis- and AI-planning-based approaches, recent works investigated methods to support engineers with the automation of design, planning, and execution of multi-robot cells. However, real-time constraints and stochastic processes were not well covered due, e.g., to the high abstraction level of the problem modeling, and these methods do not scale well. In this paper, using probabilistic model checking, we construct a controller and integrate it with reinforcement learning approaches to synthesize the most efficient and correct multi-robot task schedules. Statistical Model Checking (SMC) is applied for system requirement verification. Our method is aware of uncertainties and considers robot movement times, interruption times, and stochastic interruptions that can be learned during multi-robot cell operations. We developed a model-at-runtime that integrates the execution of the production cell and optimizes its performance using a controller-based AI system. For this purpose and to derive the best policy, we implemented and compared AI-based methods, namely, Monte Carlo Tree Search, a heuristic AI-planning technique, and Q-learning, a model-free reinforcement learning method. Our results show that our methodology can choose time-efficient task sequences that consequently improve the cycle time and efficiently adapt to stochastic events, e.g., robot interruptions. Moreover, our approach scales well compared to previous investigations using SMC, which did not reveal any violation of the requirements.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Artificial intelligence
- Informatik (insg.)
- Software
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
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- BibTex
- RIS
AAMAS '24: Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems. Hrsg. / Mehdi Dastani; Jaime Simao Sichman. 2024. S. 1928-1937 (Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Multi-Robot Motion and Task Planning in Automotive Production Using Controller-based Safe Reinforcement Learning
AU - Wete, Eric
AU - Greenyer, Joel
AU - Kudenko, Daniel
AU - Nejdl, Wolfgang
N1 - Publisher Copyright: © 2024 International Foundation for Autonomous Agents and Multiagent Systems.
PY - 2024/5/6
Y1 - 2024/5/6
N2 - Using synthesis- and AI-planning-based approaches, recent works investigated methods to support engineers with the automation of design, planning, and execution of multi-robot cells. However, real-time constraints and stochastic processes were not well covered due, e.g., to the high abstraction level of the problem modeling, and these methods do not scale well. In this paper, using probabilistic model checking, we construct a controller and integrate it with reinforcement learning approaches to synthesize the most efficient and correct multi-robot task schedules. Statistical Model Checking (SMC) is applied for system requirement verification. Our method is aware of uncertainties and considers robot movement times, interruption times, and stochastic interruptions that can be learned during multi-robot cell operations. We developed a model-at-runtime that integrates the execution of the production cell and optimizes its performance using a controller-based AI system. For this purpose and to derive the best policy, we implemented and compared AI-based methods, namely, Monte Carlo Tree Search, a heuristic AI-planning technique, and Q-learning, a model-free reinforcement learning method. Our results show that our methodology can choose time-efficient task sequences that consequently improve the cycle time and efficiently adapt to stochastic events, e.g., robot interruptions. Moreover, our approach scales well compared to previous investigations using SMC, which did not reveal any violation of the requirements.
AB - Using synthesis- and AI-planning-based approaches, recent works investigated methods to support engineers with the automation of design, planning, and execution of multi-robot cells. However, real-time constraints and stochastic processes were not well covered due, e.g., to the high abstraction level of the problem modeling, and these methods do not scale well. In this paper, using probabilistic model checking, we construct a controller and integrate it with reinforcement learning approaches to synthesize the most efficient and correct multi-robot task schedules. Statistical Model Checking (SMC) is applied for system requirement verification. Our method is aware of uncertainties and considers robot movement times, interruption times, and stochastic interruptions that can be learned during multi-robot cell operations. We developed a model-at-runtime that integrates the execution of the production cell and optimizes its performance using a controller-based AI system. For this purpose and to derive the best policy, we implemented and compared AI-based methods, namely, Monte Carlo Tree Search, a heuristic AI-planning technique, and Q-learning, a model-free reinforcement learning method. Our results show that our methodology can choose time-efficient task sequences that consequently improve the cycle time and efficiently adapt to stochastic events, e.g., robot interruptions. Moreover, our approach scales well compared to previous investigations using SMC, which did not reveal any violation of the requirements.
KW - Model Checking
KW - Multi-robot Motion Planning
KW - Multi-robot Task Planning
KW - Q-Learning
KW - Safe Reinforcement Learning
UR - http://www.scopus.com/inward/record.url?scp=85196424584&partnerID=8YFLogxK
U2 - 10.5555/3635637.3663056
DO - 10.5555/3635637.3663056
M3 - Conference contribution
AN - SCOPUS:85196424584
SN - 9798400704864
T3 - Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
SP - 1928
EP - 1937
BT - AAMAS '24
A2 - Dastani, Mehdi
A2 - Sichman, Jaime Simao
T2 - 23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024
Y2 - 6 May 2024 through 10 May 2024
ER -